Michael is a leading researcher in the field of artificial intelligence specializing in the intersection of deep learning and biotechnology. His research focuses on the computational design and optimization of proteins, aptamers, and small molecules, using advanced algorithms Machine learning is being used to accelerate the discovery and engineering of novel biomolecules with therapeutic, diagnostic, and industrial applications Michael develops and applies novel deep learning models—including generative models, protein language models, and graph neural networks—to predict molecular structures, enhance molecular binding, and investigate functional mutations. By combining data-driven methods with biophysical and biochemical principles, he is playing key role in the evolution of synthetic biology and performs precision medicine